Device and method for monitoring an electric power station

ABSTRACT

The invention concerns a method and a corresponding device for monitoring a technical installation. The invention is characterized in that a dynamic model of at least one system of the technical installation is enhanced by means of an artificial intelligence based algorithm during the operation of said system.

CROSS REFERENCE TO RELATED APPLICATION

This application is the US National Stage of International ApplicationNo. PCT/EP2003/007202, filed Jul. 4, 2003 and claims the benefitthereof. The International Application claims the benefits of EuropeanPatent application No. 02021501.8 EP filed Sep. 26, 2002, both of theapplications are incorporated by reference herein in their entirety.

FIELD OF THE INVENTION

The invention relates to a device as well as a method for monitoring atechnical facility comprising multiple systems, in particular a powerplant facility.

BACKGROUND OF THE INVENTION

Conventional devices and methods for monitoring a technical facilitycomprising multiple systems, in particular diagnostic methods anddiagnostic equipment, are often based on the observation and/ormeasurement of specific operational parameters of the technicalfacility, whereby exceeding or falling short of a reference value callsfor a maintenance procedure.

Naturally, the derivation of a necessary operational procedure byobserving parameters measured in isolation is, at the same time,imprecise and prone to errors.

If, on the other hand, an abundance of data that accumulates in thetechnical facility, in particular measurement values from variousmeasurement positions and/or corresponding stored historical measurementvalues, is consulted in order to create a picture of the present orfuture expected operational status, then this likewise leads to nosatisfactory conclusion, because the mutual dependencies of this datafrom data sources which are generally highly diverse are mostly unknown,and therefore, likewise, no precise evaluation or even prediction of theoperational situation from it is possible.

In addition, it is to be expected that not all data that exerts aninfluence on the operational situation of the facility is included,which makes the problem even more complicated.

SUMMARY OF THE INVENTION

As a result, the object of the invention is to show an improved deviceas well as a method for monitoring a technical facility comprisingmultiple systems, in particular a power plant facility. At the sametime, high prediction accuracy in particular should be achievable withregard to a developing failure in the technical facility.

In addition, so-called “creeping process deviations” that lead away froma desired operational situation and practically always precede theappearance of a failure and/or a process disruption should be able to beidentified as early in time as possible.

Furthermore, the expected point in time of the appearance of a failureshould be identifiable as early in time as possible by means of a deviceaccording to the invention or a corresponding method, such thatcountermeasures (for example, a maintenance procedure) can be initiatedbefore a failure of the facility or its components occurs.

In addition, a device according to the invention as well as acorresponding method should reduce the expense of diagnosticapplications in the facility that have been customary up until now, andfurthermore allow a better optimization of the control devices used.

The object according to the invention with regard to the device isachieved by means of a device for monitoring a technical facilitycomprising multiple systems, in particular a power plant facility,including

at least one analysis module, which includes a dynamic model of at leastone system of the technical facility, whereby operational and/orstructural data for the technical facility can be supplied to theanalysis module as input data, and

at least one algorithm based on artificial intelligence incorporated bythe analysis module, by means of which the dynamic model of the systemcan be improved during the operation of the system, whereby output datais identifiable by means of the analysis module, which characterizes thepresent and/or future operational behavior of the system.

At the same time, the invention starts from the consideration that, withconventional modeling known from the prior art, the achievable precisionand thus the achievable degree of agreement with the correspondingactual measurements for the identified model measurements is too limitedto come to reliable conclusions about a future behavior of the facility.At the present time, known modelings offer the most suitable results,i.e. there exists a high degree of agreement with the correspondingactual measurements at the present time. Therefore, the further in thefuture that the relevant point in time for the behavior of the facilitylies, the greater the unreliability of the prediction.

An additional starting point for the invention lies in the realizationthat, in many cases, it is impossible or possible only at extremelygreat expense to specify a fairly accurate model for the technicalfacility (for example, because of a strongly non-linear behavior ofcertain systems of the technical facility).

With the device according to the invention, a dynamic model of at leastone system of the technical facility is assumed, which is improvedduring operation by means of methods of artificial intelligence. Thecapability of the analysis module to describe and to forecast theoperational behavior of the system is thereby improved.

At the same time, it is not urgently necessary to start with a complex,uniform dynamic model of the system. For example, a set of a fewinsular, simple equations and characteristics, which can be supplementedby means of a neural network (preferably structured in a simple manner),fuzzy logic or a genetic algorithm, often suffices. The interactionbetween these “partial models” for a system description is then improvedduring operation by means of the algorithm based on artificialintelligence such that an interrelationship develops for the saidelements.

A model, particularly a deterministic one in the classical sense, is notnecessary. On the contrary, the mentioned interrelationship isparameterized (for example, a Bernoulli equation for this relationshipin order to use it on a specific existing flow), and the algorithm basedon artificial intelligence searches in historical or present operationaldata and/or structural data of the system and/or of the technicalfacility for correlations, for example changes in measurements that arecreated as a consequence of the change in other measurements.Newly-discovered correlations of this type are then integrated into thedynamic model by means of the algorithm based on artificialintelligence—in particular as an additional characteristic and/orequation or as an adjustment of parameters of the dynamic model, forexample of the network weight factors of a neural network—and these arethereby improved.

In the context of the invention, the term “system” should cover therange from a simple component—for example, a pipeline—up to a highlycomplex complete system, including a number of subsystems—for example aturbine set, a boiler facility, a power plant block or the completepower plant.

The term “operational data” is to be understood to mean, in particular,all types of data that accrue during the operation of the technicalfacility such as, for example, temperature measurements, pressuremeasurement data, thermal images, sensor data, messages, alarms,warning, and so forth.

The “algorithm based on artificial intelligence” includes, inparticular, methods of artificial intelligence such as neural networks,fuzzy logic and genetic algorithms.

The “dynamic model” can be described deterministically and numericallyor also by means of methods based on artificial intelligence.Furthermore, it can include physical and mathematical equations.Combinations of the mentioned elements are also included, in particularphysical and/or mathematical equations that are linked by means ofmethods based on artificial intelligence.

In a preferred embodiment, the improvement of the dynamic model includesthe identification of that input data which has not yet been previouslyused by the dynamic model, and the dynamic model can be expanded withthe help of this input data.

At the same time, the algorithm based on artificial intelligence is usedfor the improvement of the dynamic model for the identification andestablishment of correlations not yet considered in the dynamic model.

The dynamic model preferably includes one or more elements from thegroup {characteristic, physical equation, neural network, fuzzy logic,genetic algorithm}.

The dynamic model particularly includes at least one neural network,which can be trained with historical operational data from the system.

The modeling of technical components and facilities by means of neuralnetworks is a known and proven procedure. A particular advantage to beseen therein is that an analytical description of the components to bemodeled need not be known. The structure of the neural network firstinitialized by means of initial parameters (“initial weighting factors”)and determined in advance through the training phase (which, forexample, includes a known backpropagation algorithm) is designed withrespect to its weighting factors such that a good correlation with theactual component can be expected after the conclusion of the trainingphase. In this manner one obtains a model of the component without beingrequired to undertake a precise analytical analysis. In the trainingphase, the neural network learns to respond to specific input valueswith specific output values; together with their corresponding outputvalues, input values of this type are often designated as a trainingset. In operation, the neural network then interpolates for input valuesthat are not included in the training set, such that output values arealso calculated for input values of this type.

During operation of the technical facility, the problem often appearsthat not all operational data that exerts an influence on the behaviorof the component(s) to be modeled (or the entire technical facility aswell) are known or ascertainable.

Furthermore, the use of at least one algorithm based on artificialintelligence makes it possible, by means of the dynamic model, toincorporate into the calculations of the status of the system of thetechnical facility those parameters that do not act directly on thissystem of the technical facility, for example as input and/or outputsignals or media flows. For example, for a serially ordered chain ofsystems, the modeling of a system located in the middle of this chain isprovided, which—alongside input signals where appropriate actingdirectly upon this system—derive input signals from the preceding systemthat are not measurable or available in other ways.

At the same time, the methods of artificial intelligence (which can bedesigned according to biological evolution, for example, as geneticsearch algorithms based on a suitable characteristic combination) alsothen allow a calculation of the status of a system of the technicalfacility if the input parameters for the determination of the currentstatus are largely unknown or identifiable only with difficulty, forexample—as mentioned before—by means of a complex measurement of theoutput values of the preceding system.

For example, statistical methods can also be used at the same time inconnection with the algorithm based on artificial intelligence, wherebythe most probable input and/or output values for a system that are nototherwise accessible are used in a current operational situation inwhich the algorithm based on artificial intelligence determines theseinput and/or output values of the respective system which are requiredby the dynamic model, for example, through an evolutionary searchstrategy.

In this manner, a good correlation for the model of at least one systemcan be expected with the actual behavior of this system, because alsoincluded in the modeling of the system by means of at least onealgorithm based on artificial intelligence is that operating data thatwould otherwise be ignored and would lead to a more or less high levelof imprecision for the model and, for this reason especially, thepredictions created with it.

Input and/or output data for the system that determines its operationalstatus, but is not accessible, for example, by means of measurement, isalso included for this reason in particular. Thus, the precision of theprediction is increased.

A particularly preferred embodiment of the invention consists of anumber of analysis modules, which each include a dynamic model of atleast one system of the technical facility. Furthermore, at least oneadditional algorithm based on artificial intelligence is provided at thesame time, by means of which correlations at least between the inputand/or output data of a first of the analysis modules and the inputand/or output data of a second of the analysis modules is identifiable.

This embodiment of the invention relates to the expansion of the deviceaccording to the invention to parallel monitoring of interactingsystems, whereby the interaction in the form of a relationship betweenthe respective input and/or output data of the analysis modules isdetermined from additional algorithms based on artificial intelligenceand is established as additional correlations (for example, in the formof an equation, a neural network or a characteristic).

Thus develops a precise dynamic model of the interactive systemscomprising the dynamic model of the individual systems as well as theadditional correlations.

Thus, the current and/or future operational behavior of the individualsystems as well as the operational behavior of the facility resultingfrom the interaction of the systems can be described.

Advantageously identifiable at the same time by means of thecorrelations is additional output data that characterizes the currentand/or future operational behavior of the technical facility, wherebythis additional output data includes cross-system information.

Correlations between the said data indicate mutual dependencies, bywhich the additional output data thus extracted goes beyond the systemlimits of the individual systems concerned in its informative value, andthus describes the behavior of a larger unit of the technical facilityconsisting of at least two systems.

Preferably, the operational and/or structural data of the technicalfacility includes one or more items of information from the group{process data, operational messages, warning messages, disruptionmessages, monitoring notifications, comments, design of the technicalfacility, hierarchy of the facility components}.

At the same time, the process data can be acquired online and offlinefrom a control system of the technical facility and/or a subsystemassociated with it, or also manually entered.

The operational messages particularly include sensor data andinformation derived therefrom about the operational status of thetechnical facility and its systems.

The structural data particularly includes information about the designof the technical facility with regard to the systems comprising thetechnical facility (facility components, subsystems, system groups) aswell as their hierarchical interaction and prioritization.

At the same time, this data can include current and/or historical data,which, for example, is recorded in a short- or long-term archive or inan engineering system.

The operational and/or structural data is preferably supplied by aprocess control system.

For operating and monitoring complex technical facilities, a processcontrol system in which the mentioned data is available or accruesduring operation and is stored is typically used. In this embodiment,the data provision is therefore of especially low complexity.

Furthermore, the invention leads to a method for monitoring a technicalfacility comprising multiple systems, particularly a power plantfacility, including the following steps:

Operational and/or structural data from the technical facility isprovided as input data to a dynamic model of at least one system of thetechnical facility,

the dynamic model of the system is improved during the operation of thesystem by means of an algorithm based on artificial intelligence, and

output data that characterizes the current and/or future operationalbehavior of the system is determined by means of the dynamic model.

The improvement of the dynamic model preferably includes theidentification of that input data that has not yet been previously usedby the dynamic model, and the dynamic model can be expanded with thehelp of this input data.

In a further embodiment, a number of dynamic models are provided that ineach case describe at least one system of the technical facility and atleast one additional algorithm based on artificial intelligence, bymeans of which correlations at least between the input and/or outputdata of a first of the dynamic models and the input and/or output dataof a second of the dynamic models are identifiable.

Advantageously identifiable by means of the correlations is additionaloutput data that characterizes the current and/or future operationalbehavior of the technical facility, whereby this additional output dataincludes cross-system information.

The explanations provided in connection with the device according to theinvention and its advantageous embodiments are transferable to themethod according to the invention and are therefore not repeated here.

In summary, the invention can be incorporated into the followingenvironment:

Artificial intelligence for the diagnosis of systems of a technicalfacility, for example a power plant facility, can be used to predictforeseeable failures, whereby all data available in the technicalfacility can be consulted.

At the same time, the main points of emphasis lie, for example, ingenetic algorithms and neural networks for modeling and accomplishingthe monitoring tasks, particularly diagnosis tasks.

It is of particular interest to decidedly reduce the expense ofdiagnosis applications in the technical facility, and furthermore tomake possible an improved optimization of the controls.

An improvement is achieved if, on the one hand, the relevant aggregatecharacteristics of systems of the technical facility, for example, powerand energy consumption, are reduced with regard to legal regulations andresource scarcity. On the other hand, customer wishes for improvedperformance and diagnosis facilities are fulfilled.

Both large systems and small systems can be integrated into thediagnosis by means of genetic/evolutionary algorithms.

It is possible to facilitate conclusions about the status of at leastone system of the technical facility by associating genetic(evolutionary) algorithms with Kohonen networks and/or neural networksof any type.

The use of genetic algorithms therefore also makes it possible toinclude in the determination of the status of a system of the technicalfacility those parameters that do not directly affect this component ofthe technical facility, for example as input and/or output signals ormedia flows.

The methodologies of genetic algorithms (search algorithms) thereforealso then allow a calculation of the status of at least one system or ofthe entire technical facility if the input parameters for thedetermination of the current status are largely unknown and/or notidentifiable or identifiable only with difficulty, for example, by meansof an expensive measurement.

Furthermore, the use of artificial intelligence for the diagnosis makesit possible for deviations from calculated current statuses to bereported to the operator of the technical facility in the case ofcomplex facility statuses.

A specific failure notice, for example about the narrowly isolatedfailure location, can be initially dispensed with in this case, becausefailures, for example of sensors, are usually measured and reported inany event by an existing control system.

What is instead important in connection with the invention is theidentification of creeping processes—that do not necessarily causeimmediate failure of a facility component—such as contamination, loss ofpower through wear, ageing and so forth, which are incorrectly perceivedor incorrectly interpreted by people because of the “habituationeffect”.

In many cases, creeping changes of this type sometimes lead to thefailure of the technical facility. The changes are, however, often notidentified because an existing control device, for example, attempts tocounter this change. Contamination on the blades of a fan is compensatedfor by re-adjusting the fan blades, for example. Or the control devicecompensates for decreased outputs in oil pumps or coolant pumps throughnew reference value specifications; then the temperature of a bearing,for example, becomes higher only very slowly, because the control devicecan often delay the moment of system failure in the case of an impendingfailure. At the same time, however, ever more is demanded of thecontrolled systems, and the wear increases. The user of the technicalfacility notices nothing as a result, because, according to the controldevice, the technical facility continues to function even though one ormore systems of the technical facility get closer to their wear limit.

A risky operation exists in particular if a functioning system isoperated under increased stress; such stress can be generated through acontrol device mentioned previously by means of a reference valuespecification.

For example, a coolant loop is constructed for continuous operation at50% output. Permanent operation at 70–80% output can then soon lead toserious damages. An impending failure of a coolant pump stricken with aleak remains unnoticed, however, because the control device increasesthe reference value (i.e. the pressure) for the coolant pump more andmore to maintain the function of the coolant loop, which accelerates thefailure of the pump further. Not until the failure actually occurs isthe failure of the coolant pump noticed as the source of the failure ofthe cooling system. A device according to the invention as well as amethod can provide a remedy here.

Furthermore, genetic algorithms in conjunction with intelligent,adaptive networks allow the identification of risky operating modes ofthe technical facility, capacity overload or incorrect capacityutilization of units and systems, and so forth. This is advantageouslyreported to the operator/user of the technical facility, for example inthe form of an operational diagram (i.e. a characteristic diagram), fromwhich results the current operation as well as a proposed improvedoperation.

The presentation of deviations can occur advantageously by means ofcharacteristic diagrams. In addition to the failure prediction, anoptimization of the operation of the operation of the technical facilityis also possible based on genetic algorithms.

Furthermore, information can be extracted by means of genetic algorithmsfor the management personnel of the technical facility, which makespossible a conclusion about the overall status of the facility and ifappropriate about maintenance procedures necessary within a timeinterval.

The use of artificial intelligence advantageously makes possible anonline calculation of system statuses, i.e. the operator can be notifiedof a “failure behavior” in his facility and is then able to makeanticipatory calculations that make a new approach possible for him.

EXAMPLE

A device according to the invention, for example in the form of adiagnostic system, reports “Failure in coal pulverizer XX grindingrollers zone” to the operator; it is determined through counterchecksthat a maintenance procedure for the coal pulverizer is necessary (forexample, because this is prescribed by the manufacturer in the relatedmaintenance manual).

By means of anticipatory calculation, it can be determined by means ofthe diagnostic system according to the invention what would occur if theoperator nevertheless leaves his technical facility in operation withoutmaintenance procedures and when the actual appearance of an operationalfailure of the coal pulverizer is to be expected.

With the combination of genetic algorithms and neural networks as wellas optional Kohonen networks, a multitude of conclusions can be reachedwith respect to the current and/or future status of the technicalfacility, in particular when a maintenance procedure will be necessary.

BRIEF DESCRIPTION OF THE DRAWINGS

In the following two exemplary embodiments of the invention aredescribed in more detail. They show:

FIG. 1 a system hierarchy, as customarily occurs in technicalfacilities,

FIG. 2 a device according to the invention, and

FIG. 3 a further embodiment of a device according to the invention withtwo analysis modules.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 shows by way of example a hierarchical system design of atechnical facility 2.

The technical facility 2 is designed as a power plant facility for thegeneration of electrical energy and includes two power plant blocks 3.

Each power plant block 3 includes two turbines 5, for example gasturbines. These turbines 5 in turn each contain a coolant loop 9.

This coolant loop 9 includes a turbine blade 11 of the turbine 5.

Each of the mentioned elements should fall under the term system inconnection with the invention. A system can therefore include a simple,isolated component such as, for example a turbine blade, as well as acomplex system, such as the power plant block 3 or multiple power plantblocks 3.

FIG. 2 shows a device 1 according to the invention with an analysismodule 13.

At the same time, operational data 17 and structural data 19 from thetechnical facility is conveyed to the analysis module 13 as input data.

The operational data 17 can for example involve online measurement datawhich is recorded in the technical facility in the system itself bymeans of sensors. At the same time, it can also involve data derivedfrom this measurement data, which is produced in a computer system, forexample. Furthermore, the operational data 17 can also include offlinemeasurement data, which is stored in an archive or manually entered, forexample.

The structural data 19 describes the technical facility or the systemitself. In particular, it includes information about the interconnectionof subsystems that are included in the system and their hierarchicalarrangement.

A dynamic model 15 is provided for modeling the system behavior. Thismodel 15 can include analytic equations, for example, as well as methodsof artificial intelligence such as, for example, neural networks, fuzzylogic or genetic algorithms. Furthermore, simple characteristics can inparticular be provided for the description of the system behavior.

An algorithm 21 based on artificial intelligence is provided for theimprovement of the dynamic model 15 during the operation of the system15.

This algorithm 21 based on artificial intelligence can be designed as agenetic algorithm, for example.

An important role for this algorithm 21 consists in effecting dynamicadjustments in the model 15 in order to achieve an improvement of thismodel 15 in the sense that an improved model behavior and thus a bettercorrelation with the behavior of the actual system is achieved. Forexample, a modeling error can be called upon for the evaluation of thiscircumstance, for example, the difference between the actualchronological behavior of the system and the modeled chronologicalbehavior of this system. An improvement of the model 15 can then takeplace by means of the algorithm 21 based on artificial intelligence. Atthe same time, the algorithm 21 based on artificial intelligence isparticularly used to identify parameters and data not yet consideredduring modeling which are included in the operational data 10 and/or thestructural data 19 but have not yet been called upon for modeling, andto establish further correlations, for example equations orcharacteristics, including the mentioned identified parameters and/ordata, and to add them to the dynamic model 15.

An algorithm 21 based on artificial intelligence designed as a geneticalgorithm optimizes correlations included in the dynamic model 15 suchas, for example, equations, characteristics or network parameters of aneural network, in that it combines and re-combines evolutionaryparameters and data and, at the same time, discovers new correlations inparticular, which are not yet included in the dynamic model 15.

In this respect, the described modeling used in connection with theinvention and its improvement by means of the algorithm 21 based onartificial intelligence goes beyond known methods of, for example,supervised learning and classical modeling.

The analysis model 13 produces conclusions about the operationalbehavior of the system as output data 23. At the same time, for example,it can involve current or future operational behavior of the system(creation of a prediction). For example, operational data 17 is conveyedto the analysis module 13, and it is assumed that this operational datawill persist over a particular future time period. The output data 23then allows a conclusion, for example, as to whether and, as the casemay be, when a disruption of the system's operation is to be expected.The more precisely the model 15 reflects the actual system behavior, themore precise is this conclusion. A high level of precision for the model15 is provided by device 1 according to the invention, in particular bymeans of the algorithm 21 based on artificial intelligence, such thatthe predictions and diagnoses determined as output data 23 by theanalysis module 13 are very precise.

The output data 23 includes, in particular, qualified reports withregard to failure identification (trend analysis, wear and ageing),efficiency, process quality and expected future behavior of the systemand the technical facility.

In order to produce reports of this type, a set of rules can be includedin analysis module 13 in order to transform output data generated fromthe model 15 into the mentioned reports. At the same time, the set ofrules can include rules for the prediction of a short-term observationperiod in particular as well as rules for a long-term observationperiod.

At the same time, in addition to the output data from the model 15,additional information can be conveyed to the set of rules, for examplereports and alarms related to the system or the technical facility.

In the illustration for FIG. 3, a device 1 according to the inventionincludes two analysis modules 13 a and 13 b.

At the same time, operational data 17 a and structural data 19 a from acoolant system 29 is conveyed to the analysis module 13 a; the analysismodule 13 b receives operational data 17 b and structural data 19 b froma generator 31 as input data.

In addition, environmental data 33 for the technical facility isconveyed to both analysis modules 13 a, 13 b, for example the ambienttemperature, atmospheric humidity, atmospheric pressure and so forth.

Each analysis module 13 a, 13 b detects output data 23 a or 23 b, whichcharacterizes the operational behavior of the respective analyzed system29 or 31.

Because the coolant system 29 and the generator 31 are to be consideredsystems not procedurally isolated from one another, it is to beanticipated that changing operational data 17 a from the coolant system29 in particular influences the system behavior of the generator 31, andthus the output data 23 b from the analysis module 13 b. The sameapplies for changing operational data 17 b from the generator 31, fromwhich it can be expected that the operational behavior of the coolantsystem 29 and thus the output data 23 a from the analysis module 13 achanges as a result.

In order to detect and quantify correlations of this type, theadditional algorithm 25 based on artificial intelligence is provided.

This can be designed, for example, as an additional genetic algorithmthat produces additional output data 27, which includes cross-systeminformation, and thus goes beyond the characterization of the behaviorof one of the systems, and in particular contains information about theinteraction of the systems 29 and 31 and their mutual dependencies.

At the same time, the additional algorithm 25 based on artificialintelligence is therefore responsible for identifying and establishinghigher-level cross-system correlations. These correlations can include,for example, equations, characteristics or neural networks, which areproduced and/or parameterized by the additional algorithm 25 based onartificial intelligence.

At the same time, the strategy for the identification and establishmentof cross-system correlations of this type can be similar to theidentification and establishment of additional internal systemcorrelations mentioned in connection with FIG. 2 by means of thealgorithms 21 a, 21 b based on artificial intelligence.

By using a device according to the invention and a method according tothe invention, it should be possible, in particular, to createconclusions about the system behavior, in particular about that in thefuture, from a system's existing operational and structural data withoutcomplex diagnostic instruments.

A self-adaptive dynamic model of the system is provided for thispurpose, which is improved during operation by an algorithm based onartificial intelligence.

The algorithm 21 based on artificial intelligence is used in particularfor searching for correlations in a technical facility's operationaland/or structural data that is typically available and processed in acontrol system, for example, and for integrating into the dynamic modelthe correlations identified in doing so in order to improve the saidmodel incrementally.

It is therefore not necessary that an analytical model of the system orof the technical facility exists. Instead, the model based on, forexample, a very simple characteristic from a characteristic array and/oron simple equations, is improved incrementally by means of a correlationanalysis of the operational and structural data by means of thealgorithm based on artificial intelligence by establishing thecorrelations determined in doing so, for example, in the form ofadditional characteristics, equations, and so forth.

In contrast to conventional monitoring and diagnostic devices, thepresent is preferably based on a data-based method, whereby dependenciesbetween parts of existing operational data and/or between parts ofstructural data from a technical facility are detected using methods ofartificial intelligence and established as quantified correlations, forexample equations and/or characteristics, such that a precise dynamicmodel of at least one system of the technical facility is produced.

1. An electric power system comprising: a power plant block; and adevice, including a computer system, coupled to receive data from thepower plant block, the device including an analysis module for:providing a dynamic model of a system of the power plant block,configured to generate output data based on the data received from thepower plant block; and implementing at least one algorithm based onartificial intelligence, that searches for dependencies or correlationsamong data received by the device, for integrating into the dynamicmodel new correlations based on said searches to improve the dynamicmodel of the system, thereby enabling provision of output dataindicating changes in current or future operational behavior of thepower plant block.
 2. The system according to claim 1, wherein animprovement of the dynamic model is based on continual acquisition ofoperational or structural data associated with a system in the powerplant block, including data not previously used by the dynamic model,which data forms a basis to modify the dynamic model.
 3. The systemaccording to claim 1, wherein the dynamic model further comprises anelement from the group consisting of: a physical equation, a neuralnetwork, fuzzy logic, and a genetic algorithm.
 4. The system accordingto claim 1, wherein the dynamic model includes an neural network that istrained using historical operational data from the system.
 5. The systemaccording to claim 1, wherein the device is configurable to include aplurality of analysis modules each including a dynamic model of a systemof the power plant block and with said at least one algorithm based onartificial intelligence capable of providing correlations between theinput and output data of a first of the analysis modules and the inputand/or output data of a second of the analysis modules.
 6. The systemaccording to claim 5, wherein the device is configurable tocharactrerize future operational behavior of the power plant block basedon cross-system information.
 7. The system according to claim 1, whereinthe device is configured to process data from the group consisting of:process data, operational messages, warning messages, disruptionmessages, monitoring notifications, comments, design of the electricpower station, hierarchy of the electric power station, and combinationsthereof.
 8. The system according to claim 1, wherein the device isconfigured to process current and historical data associated with theplant power block.
 9. The system according to claim 1, wherein the plantpower block comprises a process control system coupled to provideoperational data and structural data, derived from multiple systems ofthe plant power block, to the device.
 10. The system according to claim1, wherein the device is configurable with the algorithm based onartificial intelligence to develop relationships among individualcorrelations of the dynamic model and develop new parametric valuesbased thereon.
 11. A method for monitoring a system of the typeincluding a power plant block, comprising: acquiring system data duringoperation of the power plant block; providing the data to a computersystem; using the data to exercise a first dynamic model of at least onesystem of the power plant block to provide output data indicative ofplant operation; modifying the dynamic model, with at least onealgorithm based on artificial intelligence that searches fordependencies or correlations among acquired data, by integrating intothe dynamic model new correlations based on said searches; and providingthe output data based on the modification of the dynamic model tocharacterize current or future operational behavior of the power plantblock.
 12. The method according to claim 11, wherein the step ofmodifying the dynamic model includes acquiring input data which has notbeen previously used by the dynamic model.
 13. The method according toclaim 11, wherein a plurality of additional dynamic models areexercised, each model describing operation of a system of the powerplant block and wherein the step of modifying the first dynamic modelincludes developing correlations based on input data associated with thefirst dynamic model and input data associated with one of the additionaldynamic models.
 14. The method according to claim 13, characterized inthat the step of modifying the first dynamic model includes developingcorrelations between output data associated with the first dynamic modeland said one of the additional dynamic models whereby the output dataincludes cross-system information.